When Tesseract Brings Friends: Layout Analysis, Language
Identification, and Super-Resolution in the Optical Character
Recognition of Medieval Texts

D 2021

When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts

NOVOTNÝ, Vít, Kristýna SEIDLOVÁ, Tereza VRABCOVÁ a Aleš HORÁK

Základní údaje

Originální název

When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts

Autoři

NOVOTNÝ, Vít (203 Česká republika, garant, domácí), Kristýna SEIDLOVÁ (203 Česká republika, domácí), Tereza VRABCOVÁ (203 Česká republika, domácí) a Aleš HORÁK (203 Česká republika, domácí)

Vydání

Brno, Recent Advances in Slavonic Natural Language Processing (RASLAN 2021), od s. 29-39, 11 s. 2021

Nakladatel

Tribun EU

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Full text PDF Domovská stránka workshopu

Kód RIV

RIV/00216224:14330/21:00119901

Organizační jednotka

Fakulta informatiky

ISBN

978-80-263-1670-1

ISSN

Klíčová slova anglicky

Optical character recognition · Layout analysis; Language identification; Image super-resolution; Medieval texts

Změněno: 15. 5. 2024 09:25, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

The aim of the AHISTO project is to make documents from the Hussite era (1419–1436) available to the general public through a web-hosted searchable database. Although scanned images of letterpress reprints from the 19th and 20th century are available, accurate optical character recognition (OCR) algorithms are required to extract searchable text from the scanned images. In our previous article [15], we have shown that the Tesseract 4 OCR algorithm was the second fastest and the most accurate among five different OCR algorithms. In this article, we investigate the impact of six preprocessing techniques on the accuracy of Tesseract 4. Additionally, we compare Tesseract 4 with three other OCR algorithms on the language identification task. Furthermore, we publish an open dataset [16] of scanned images and OCR texts with human annotations for layout analysis, OCR evaluation, and language identification. In Section 2, we describe the related work in OCR preprocessing. In Section 3, we describe our three preprocessing techniques and our two evaluation tasks. In Section 4, we discuss the results of our evaluation. In Section 5, we offer concluding remarks and ideas for future work in the OCR of medieval texts.

Návaznosti

LM2018101, projekt VaV

Název: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy (Akronym: LINDAT/CLARIAH-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, LINDAT/CLARIAH-CZ - Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy

Citovat

NOVOTNÝ, Vít, Kristýna SEIDLOVÁ, Tereza VRABCOVÁ a Aleš HORÁK. When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts. In Horák, Rychlý, Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2021). Brno: Tribun EU, 2021, s. 29-39. ISBN 978-80-263-1670-1.

@inproceedings{1809738,
   author = {Novotný, Vít and Seidlová, Kristýna and Vrabcová, Tereza and Horák, Aleš},
   address = {Brno},
   booktitle = {Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)},
   editor = {Horák, Rychlý, Rambousek},
   keywords = {Optical character recognition · Layout analysis; Language identification; Image super-resolution; Medieval texts},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-263-1670-1},
   pages = {29-39},
   publisher = {Tribun EU},
   title = {When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts},
   url = {https://nlp.fi.muni.cz/raslan/raslan21.pdf#page=37},
   year = {2021}
}

TY  - JOUR
ID  - 1809738
AU  - Novotný, Vít - Seidlová, Kristýna - Vrabcová, Tereza - Horák, Aleš
PY  - 2021
TI  - When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts
PB  - Tribun EU
CY  - Brno
SN  - 9788026316701
KW  - Optical character recognition · Layout analysis
KW  - Language identification
KW  - Image super-resolution
KW  - Medieval texts
UR  - https://nlp.fi.muni.cz/raslan/raslan21.pdf#page=37
N2  - The aim of the AHISTO project is to make documents from the Hussite era (1419–1436) available to the general public through a web-hosted searchable database. Although scanned images of letterpress reprints from the 19th and 20th century are available, accurate optical character recognition (OCR) algorithms are required to extract searchable text from the scanned images. In our previous article [15], we have shown that the Tesseract 4 OCR algorithm was the second fastest and the most accurate among five different OCR algorithms. In this article, we investigate the impact of six preprocessing techniques on the accuracy of Tesseract 4. Additionally, we compare Tesseract 4 with three other OCR algorithms on the language identification task. Furthermore, we publish an open dataset [16] of scanned images and OCR texts with human annotations for layout analysis, OCR evaluation, and language identification. In Section 2, we describe the related work in OCR preprocessing. In Section 3, we describe our three preprocessing techniques and our two evaluation tasks. In Section 4, we discuss the results of our evaluation. In Section 5, we offer concluding remarks and ideas for future work in the OCR of medieval texts.
ER  -

NOVOTNÝ, Vít, Kristýna SEIDLOVÁ, Tereza VRABCOVÁ a Aleš HORÁK. When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts. In Horák, Rychlý, Rambousek. \textit{Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)}. Brno: Tribun EU, 2021, s.~29-39. ISBN~978-80-263-1670-1.

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